CN109144163A - A kind of photovoltaic multimodal maximum power point tracking method based on manor population - Google Patents
A kind of photovoltaic multimodal maximum power point tracking method based on manor population Download PDFInfo
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Abstract
The photovoltaic multimodal maximum power point tracking method based on manor population that the present invention relates to a kind of, comprising the following steps: the 1) duty ratio for adjusting PWM is 0, obtains the output voltage of Boost circuit；2) population number is set；3) adaptive value for calculating each particle, rejudges according to gained adaptive value and selects global optimum's particle and global optimum's particle manor；4) the maximum value estimation range in the particle manor is determined；5) judge whether adaptive value condition of the maximum value estimation range comprising global optimum's particle be true；6) judge interparticle maximum distance dmaxLess than 0.02UocarWhether condition is true；7) each particle adaptive value is calculated, rejudges and selects global optimum's particle；8) judge the interparticle maximum distance dmaxLess than 0.01UocarWhether condition is true；9) voltage for the global optimum's particle position for keeping output voltage to be, judges whether environment mutates.
Description
Technical field
The present invention relates to field of photovoltaic power generation, more particularly to a kind of photovoltaic multimodal maximum power based on manor population
Point tracking method.
Background technique
In entire photovoltaic generating system, photovoltaic cell technology and photovoltaic conversion control technology are two big support technologies.Most
High-power point tracking is one of key technology of high-efficient photovoltaic system.It is non-thread for photovoltaic array P-U characteristic curve at present
Property feature has proposed many maximal power tracing algorithms.
Traditional algorithm mainly includes perturbation observation method, conductance increment method, short circuit current proportionality coefficient method, slip form extremum search
Deng, these algorithms mainly for without the MPPT maximum power point tracking under masking, uniform illumination mode.Photovoltaic cell be shielded locally or
Person's characteristic is inconsistent to be may cause more power extreme values and occurs, particularly with large-scale photovoltaic array, be easy in black clouds, tree shade,
The shielding status of building and dust etc. and the characteristic that multi-peak is presented.Traditional algorithm does not have global follow ability, more
Local extremum can be fallen under peak condition and leads to a large amount of energy losses.Intelligent algorithm mainly includes particle swarm algorithm, cuckoo
Algorithm, glowworm swarm algorithm, ant group algorithm, ant colony algorithm and wolf pack algorithm etc., these algorithms can track global maximum work
Rate point, but still have the shortcomings that track time length.
How when covering situation with no masking can fast track to global maximum power point, and the tracking time is not
The influence of the factors such as the situation that is masked complexity is in the urgent need to address in current photovoltaic array maximum power tracking method asks
Topic.
Summary of the invention
The present invention provides one kind can shorten the particle swarm algorithm tracking time, can reduce region of search rapidly, can fit
Answer a kind of photovoltaic multimodal maximum power point tracking method based on manor population of the photovoltaic array in different maskings.This
Inventing the technical solution to solve the above problems is:
A kind of photovoltaic multimodal maximum power point tracking method based on manor population, comprising the following steps:
1) duty ratio for adjusting PWM is 0, obtains the output voltage U of Boost circuitocar；
2) population number is set as 3, and the initial position of the particle is respectively 1, Uocar/ 3 and 2Uocar/3；The particle
Initial manor be respectively [1, Uocar/3)、[Uocar/ 3,2Uocar/ 3) and [2Uocar/ 3, Uocar)；
3) adaptive value for calculating each particle, rejudged and selected according to gained adaptive value global optimum's particle and
Global optimum's particle manor；
4) the maximum value estimation range in the particle manor is determined, the method is as follows: choose on the P-U curve of photovoltaic array
Any two points a and b, then the estimation range of the maximum value in range [a, b] is [max { P (a), P (b) }, bP (a)/a]；
5) judge whether adaptive value condition of the maximum value estimation range comprising global optimum's particle be true, tied in judgement
Fruit is in the case where being, which can actively abandon that the part better than global optimum's particle can not be generated in manor, is updated certainly
Oneself territorial border；If the determination result is NO, which will abandon the manor of oneself, and with global optimum's grain
Son divides equally global optimum's particle manor, and then global optimum's particle updates its territorial border；
6) judge interparticle maximum distance dmaxLess than 0.02UocarWhether condition is true, in the feelings that judging result is no
Under condition, 4) step is returned；In the case where the judgment result is yes, all particles lose oneself affiliated manor, according to original
The iterative manner of population is iterated, i.e., the position of oneself is updated by two extreme points, when the 1st extreme point is current
The optimal solution that particle itself is found until quarter, the 2nd extreme point optimal solution that entire population is found until being current time；Its
+ 1 iteration of middle kth finds i-th of particle rapidity of optimal solutionThe position andRenewal equation meets following formula:
In formula,Represent i-th of particle rapidity in k+1 iterative calculation；Kth represents the number of iterations, and ω is inertia weight,
c1、c2It is normal number, is respectively used to the specific gravity of adjustment individual experience and group's experience；r1、r2For the random number between (0,1)；
PbestRepresenting most has solution in population；GbestRepresent globally optimal solution；Represent the position of i-th of particle in k+1 iterative calculation
It sets；Represent the position of i-th of particle in k iterative calculation.
7) each particle adaptive value is calculated, rejudges and selects global optimum's particle；
8) judge the interparticle maximum distance dmaxLess than 0.01UocarWhether condition is true, is no in judging result
In the case where, it returns to 6) step and is iterated according to the iterative manner of predecessor group, assess each particle adaptive value, and
Update global optimum's particle；In the case where the judgment result is yes, into next step；
9) voltage for the global optimum's particle position for keeping output voltage to be, judges whether environment mutates, institute
It is as follows to state the formula for judging that environment mutates:
In formula: P' and P is respectively the power samples value in double sampling period after iteration ends, and Δ P is changed power threshold
Value.
If the determination result is NO, the global optimum's particle position for persistently keeping the output voltage to be
Voltage；In the case where the judgment result is yes, then 1) step is returned, the multimodal MPPT maximum power point tracking of a new round is restarted.
Detailed description of the invention
Fig. 1 is flow chart of the invention.
Fig. 2 is photovoltaic array structure in present example.
Fig. 3 is the maximum power point tracking system based on Boost circuit in present example.
Fig. 4 is the I-U performance diagram of photovoltaic array in present example.
Fig. 5 is the P-U performance diagram of photovoltaic array in present example.
Fig. 6 is manor formula alternative manner schematic diagram in present example.
Fig. 7 is the photovoltaic array P-U curve graph in present example under illumination mode 1.
Fig. 8 is the particle swarm algorithm tracing path figure in present example under illumination mode 1.
Fig. 9 is the manor particle swarm algorithm tracing path figure in present example under illumination mode 1.
Figure 10 is the photovoltaic array P-U curve graph in present example under illumination mode 2.
Figure 11 is the particle swarm algorithm tracing path figure in present example under illumination mode 2.
Figure 12 is the manor particle swarm algorithm tracing path figure in present example under illumination mode 2.
Figure 13 is the photovoltaic array P-U curve graph in present example under illumination mode 3.
Figure 14 is the particle swarm algorithm tracing path figure in present example under illumination mode 3.
Figure 15 is the manor particle swarm algorithm tracing path figure in present example under illumination mode 3.
Specific embodiment
With reference to the accompanying drawing and specific embodiment is further elaborated the contents of the present invention, but embodiment is only this
The better embodiment of invention, therefore all equivalence changes done according to feature described in present patent application range and principle,
It is included in the scope of the patent application of the present invention.
As shown in Figure 1, a kind of photovoltaic multimodal maximum power point tracking method based on manor population, including following step
It is rapid:
1) duty ratio for adjusting PWM is 0, obtains the output voltage U of Boost circuitocar；
2) population number is set as 3, and the initial position of the particle is respectively 1, Uocar/ 3 and 2Uocar/3；The particle
Initial manor be respectively [1, Uocar/3)、[Uocar/ 3,2Uocar/ 3) and [2Uocar/ 3, Uocar)；
3) adaptive value for calculating each particle, rejudged and selected according to gained adaptive value global optimum's particle and
Global optimum's particle manor；
4) the maximum value estimation range in the particle manor is determined, the method is as follows: choose on the P-U curve of photovoltaic array
Any two points a and b, then the estimation range of the maximum value in range [a, b] is [max { P (a), P (b) }, bP (a)/a]；
5) judge whether adaptive value condition of the maximum value estimation range comprising global optimum's particle be true, tied in judgement
Fruit is in the case where being, which can actively abandon that the part better than global optimum's particle can not be generated in manor, is updated certainly
Oneself territorial border；If the determination result is NO, which will abandon the manor of oneself, and with global optimum's grain
Son divides equally global optimum's particle manor, and then global optimum's particle updates its territorial border；
6) judge interparticle maximum distance dmaxLess than 0.02UocarWhether condition is true, in the feelings that judging result is no
Under condition, 4) step is returned；In the case where the judgment result is yes, all particles lose oneself affiliated manor, according to original
The iterative manner of population is iterated, i.e., the position of oneself is updated by two extreme points, when the 1st extreme point is current
The optimal solution that particle itself is found until quarter, the 2nd extreme point optimal solution that entire population is found until being current time；Its
+ 1 iteration of middle kth finds i-th of particle rapidity of optimal solutionThe position andRenewal equation meets following formula:
In formula,Represent i-th of particle rapidity in k+1 iterative calculation；Kth represents the number of iterations, and ω is inertia weight,
c1、c2It is normal number, is respectively used to the specific gravity of adjustment individual experience and group's experience；r1、r2For the random number between (0,1)；
PbestRepresenting most has solution in population；GbestRepresent globally optimal solution；Represent the position of i-th of particle in k+1 iterative calculation
It sets；Represent the position of i-th of particle in k iterative calculation.
7) each particle adaptive value is calculated, rejudges and selects global optimum's particle；
8) judge the interparticle maximum distance dmaxLess than 0.01UocarWhether condition is true, is no in judging result
In the case where, it returns to 6) step and is iterated according to the iterative manner of predecessor group, assess each particle adaptive value, and
Update global optimum's particle；In the case where the judgment result is yes, into next step；
9) voltage for the global optimum's particle position for keeping output voltage to be, judges whether environment mutates, institute
It is as follows to state the formula for judging that environment mutates:
In formula: P' and P is respectively the power samples value in double sampling period after iteration ends, and Δ P is changed power threshold
Value.
If the determination result is NO, the global optimum's particle position for persistently keeping the output voltage to be
Voltage；In the case where the judgment result is yes, then 1) step is returned, the multimodal MPPT maximum power point tracking of a new round is restarted.
The embodiment of the present invention is as follows:
As shown in Fig. 2, imitative 5 × 2 photovoltaic arrays used of photovoltaic array, as shown in figure 3, the maximum based on Boost circuit
Power points tracking system.In simulation model, CiTake 200 μ F, Co90 μ F, L are taken to take 0.15mH, RL120 Ω are taken, Boost circuit is opened
It closes frequency and is taken as 50kHz.
The parameter of various components uses the parameter of MSX-60: short circuit current I in simulation modelsc=3.8A, open-circuit voltage Uoc
=21.1V, maximum power point electric current Im=3.5A, maximum power point voltage Um=17.1V.It is 1000W/m that reference light, which shines,2, reference
Temperature is 25 DEG C.Photovoltaic array there are two series arm S1 and S2, the photovoltaic panel illumination of S1 branch be respectively [1000,800,
600,400,200] W/m2, the photovoltaic panel illumination of S2 branch is respectively [900,900,700,300,200] W/m2.Photovoltaic array with
The indicatrix of series arm is as shown in Figures 4 and 5, it can be seen that in masking, multimodal is presented in its indicatrix.
The electric current I of each series arm is as voltage U successively decreases as can be seen from Figure 4；For photovoltaic array
Its electric current I is also as the voltage U of photovoltaic array successively decreases.Corresponding to the corresponding electric current of free voltage a on P-U curve is (a, P
(a)) slope of the line of origin (0,0), i.e. I (a)=P (a)/a are arrived.For section [a, b] any on P-U curve, a and b
Corresponding adaptive value is respectively P (a) and P (b), any x is belonged to (a, b] then have
So the corresponding current value of a point is maximum on the section [a, b], for any two points a and b, Ke Yiyan on P-U curve
The range of the maximum value for providing this section of lattice.It is [max { P (a), P (b) }, bP that maximum value in [a, b], which obtains estimation range,
(a)/a]。
In the manor formula iterative manner, manor particle swarm algorithm gives 3 in initialization, by entire region of search enfeoffment
A particle, each particle are endowed manor attribute, i.e., each particle iterative information includes the position and the manor of oneself of oneself
Position, left margin are particle position.As shown in fig. 6, there is x in region of search1、x2And x33 particles, their manor are respectively
[a, b), [b, c) and [c, Uocar)；x2It is current optimal particle, [b, c) it is current optimal particle manor.Estimate by voltage range
Stratagem slightly judges, x1Manor [a, b) in be possible to generate be parity with or superiority over global optimum's particle, so abandoning can not in manor
Can generate better than global optimum's particle part [a, a '), next-generation x1Position become a ', manor be updated to [a ', b)；By
Voltage range estimation strategy judgement, x3Manor [c, Uocar) the optimal probable value that can be generated can not surmount global optimum's particle,
So abandoning in the manor of oneself, and fly to the manor of global optimum's particle, therefrom get new manor [c ', c), next-generation x3
Position become c ', global optimum's particle manor be updated to [b, c ').
The manor formula iterative strategy uses in early period and is based on manor formula iterative strategy, was not necessarily to multiparticle, and took 3 particles
?.The range of tracking is 0-Uocar, in order to avoid tactful short circuit current, tracking range is set as 1-U by TPSOocar.3 grains
The initial position of son is respectively 1, Uocar/ 3 and 2Uocar/3；Initial manor is respectively [1, Uocar/3)、[Uocar/ 3,2Uocar/ 3) and
[2Uocar/ 3, Uocar)。
The manor formula iterative strategy is in the later period, especially near GMPP, chases after although precision can be improved and can reduce
Track speed.Therefore, as interparticle maximum distance dmax<0.2UocarWhen, all particles lose manor attribute, according to predecessor
The iterative manner of group is iterated.As all interparticle maximum distance dmax<0.01Uocar, stop iteration, keep photovoltaic array
Voltage power supply is at the voltage corresponding to global optimum's particle.
In order to verify the rapidity and validity of improving particle swarm algorithm, herein using PSO and TPSO respectively in 3 kinds of differences
Illumination mode under be tracked emulation experiment.
Using the maximum power point tracking system shown in Fig. 3 based on Boost circuit, photovoltaic array uses Fig. 1 in system
Shown in 5 × 2 photovoltaic arrays.In simulation model, CiTake 200 μ F, Co90 μ F, L are taken to take 0.15mH, RLTake 120 Ω, Boost electricity
The switching frequency on road is taken as 50kHz.
In original PSO, in order to guarantee to search ability of searching optimum, number of particles is set as 5 and (goes here and there with photovoltaic array
It is identical to join photovoltaic module number), their initial position is successively set as 0.8Uoc、1.8Uoc、2.8Uoc、3.8UocAnd 4.8Uoc(UocFor
Monolithic open-circuit voltage under photovoltaic panel standard test condition), w=0.2, c1=0.2, c2=0.35, maximum limitation speed is 5, when
Interparticle maximum voltage difference dmax<0.01Uocar, stop iteration；Number of particles is 3 in TPSO, initial position is respectively 1,
Uocar/ 3 and 2Uocar/3；Initial manor is respectively [1, Uocar/3)、[Uocar/ 3,2Uocar/ 3) and [2Uocar/ 3, Uocar).W=
0.2, c1=0.2, c2=0.35, maximum limitation speed is 5, as interparticle maximum voltage difference dmax<0.01UocarStopping changes
Generation.
Illumination mode 1:
Under illumination mode 1, two branches only have 1 light levels, and the illumination of series arm S1 and S2 are
[1000,1000,1000,1000,1000] W/m2, environment temperature are 25 DEG C.The P-U characteristic curve of photovoltaic array as shown in fig. 7,
Only 1 peak, i.e. global maximum power point, position are (85.72V, 598.50W).The curve of pursuit of PSO and TPSO such as Fig. 8 and
Shown in Fig. 9, it can be seen that PSO needs about 1.30s, which can restrain, finds global maximum power point.And TPSO algorithm only needs
0.38s, the tracking time is only the 29.23% of PSO.
Illumination mode 2:
Under illumination mode 2, two branches are 3 light levels, and the illumination of series arm S1 and S2 are respectively
[1000,1000,750,300,300] W/m2 and [950,950,600,250,250] W/m2, environment temperature are 25 DEG C.Photovoltaic battle array
The P-U characteristic curve of column is as shown in Figure 10, have 3 peaks, position be followed successively by (31.78V, 216.01W), (51.40V,
256.10W) and (86.42V, 175.80W)；Global maximum power point is (51.40V, 256.10W).Two kinds of algorithm curve of pursuit
As is illustrated by figs. 11 and 12, it can be seen that PSO needs about 1.30s, which can restrain, finds global maximum power point.And TPSO
0.38s is only needed, the tracking time is only the 29.23% of PSO.
Illumination mode 3:
Illumination mode 3 is most complicated mode, and two branches are 5 light levels, the illumination of series arm S1 and S2
Respectively [1000,750,500,300,200] W/m2 and [1000,800,600,400,250] W/m2, environment temperature are 25 DEG C.
The P-U characteristic curve of photovoltaic array is as shown in figure 13, have 5 peaks, position be followed successively by (14.30V, 99.55W), (32.17V,
180.50W), (51.34V, 206.45W), (70.39V, 181.90W) and (88.54V, 148.51W)；Global maximum power point is
(51.34V, 206.45W).Two kinds of algorithm curve of pursuit are as shown in FIG. 14 and 15, it can be seen that PSO needs about 1.60s ability
Global maximum power point is found in enough convergences.And TPSO only needs 0.50s, the tracking time is only the 31.25% of PSO.
The above is only specific embodiments of the present invention, are not intended to restrict the invention, for those skilled in the art
For member, the invention may be variously modified and varied.All within the spirits and principles of the present invention, it is made it is any modification,
The institutes such as same replacement, improvement, are all covered by the present invention.
Claims (1)
1. a kind of photovoltaic multimodal maximum power point tracking method based on manor population, comprising the following steps:
1) duty ratio for adjusting PWM is 0, obtains the output voltage U of Boost circuitocar。
2) population number is set as 3, and the initial position of the particle is respectively 1, Uocar/ 3 and 2Uocar/3；At the beginning of the particle
Beginning manor is respectively [1, Uocar/3)、[Uocar/ 3,2Uocar/ 3) and [2Uocar/ 3, Uocar)；
3) adaptive value for calculating each particle, rejudges according to gained adaptive value and selects global optimum's particle and the overall situation
Optimal particle manor；
4) the maximum value estimation range in the particle manor is determined, the method is as follows: choose any on the P-U curve of photovoltaic array
Two o'clock a and b, then the estimation range of the maximum value in range [a, b] is [max { P (a), P (b) }, bP (a)/a]；
5) judge whether adaptive value condition of the maximum value estimation range comprising global optimum's particle be true, is in judging result
In the case where being, which can actively abandon that the part better than global optimum's particle can not be generated in manor, update oneself
Territorial border；If the determination result is NO, which will abandon the manor of oneself, and equal with global optimum particle
Divide global optimum's particle manor, then global optimum's particle updates its territorial border；
6) judge interparticle maximum distance dmaxLess than 0.02UocarWhether condition is true, if the determination result is NO,
Return to 4) step；In the case where the judgment result is yes, all particles lose oneself affiliated manor, according to predecessor group
Iterative manner be iterated, i.e., the position of oneself is updated by two extreme points, until the 1st extreme point is current time
The optimal solution that particle itself is found, the 2nd extreme point optimal solution that entire population is found until being current time；Wherein kth+
1 iteration finds i-th of particle rapidity of optimal solutionThe position andRenewal equation meets following formula:
In formula,Represent i-th of particle rapidity in k+1 iterative calculation；Kth represents the number of iterations, and ω is inertia weight, c1、c2
It is normal number, is respectively used to the specific gravity of adjustment individual experience and group's experience；r1、r2For the random number between (0,1)；PbestGeneration
Most there is solution in table population；GbestRepresent globally optimal solution；Represent the position of i-th of particle in k+1 iterative calculation；Generation
The position of i-th of particle in table k times iterative calculation；
7) each particle adaptive value is calculated, rejudges and selects global optimum's particle；
8) judge the interparticle maximum distance dmaxLess than 0.01UocarWhether condition is true, in the situation that judging result is no
Under, it returns to 6) step and is iterated according to the iterative manner of predecessor group, assess each particle adaptive value, and update complete
Office's optimal particle；In the case where the judgment result is yes, into next step；
9) voltage for the global optimum's particle position for keeping output voltage to be, judges whether environment mutates, described to sentence
The formula that abscission ring border mutates is as follows:
In formula: P' and P is respectively the power samples value in double sampling period after iteration ends, and Δ P is changed power threshold value；
If the determination result is NO, the electricity for the global optimum's particle position for persistently keeping the output voltage to be
Pressure；In the case where the judgment result is yes, then 1) step is returned, the multimodal MPPT maximum power point tracking of a new round is restarted.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111338420A (en) * | 2020-03-26 | 2020-06-26 | 西安电子科技大学 | Power optimization control method for simulated space solar power station |
| CN115437452A (en) * | 2022-09-13 | 2022-12-06 | 美世乐(广东)新能源科技有限公司 | Particle swarm-based multi-peak maximum power tracking control method |
| CN115857615A (en) * | 2023-03-02 | 2023-03-28 | 锦浪科技股份有限公司 | Improved photovoltaic MPPT control method |
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